The 2012 Atherosclerosis egg study: Plaque decreased as LDL increased with consumption of 2.3 eggs per week or more

An interesting study by David Spence and colleagues, published online in July 2012 in the journal Atherosclerosis (), has in the past and continues to attract media attention (e.g., ). The article is titled: “Egg yolk consumption and carotid plaque”. As the title implies, the study focuses on egg yolk consumption and its association with carotid artery plaque buildup.

The study argues that “regular consumption of egg yolk should be avoided by persons at risk of cardiovascular disease”. It hints at egg yolks being unhealthy in general, possibly even more so than cigarettes. Solid critiques have already been posted on blogs by Mark Sisson, Chris Masterjohn, and Zoe Harcombe (, , ), among others.

These critiques present valid arguments for why the key findings of the study cannot be accepted, especially the finding that eggs are more dangerous to one’s health than cigarettes. This post is a bit different. It uses the data reported in the study to show that it (the data) suggests that egg consumption is actually health-promoting.

I used the numbers in Table 2 of the article to conduct a test that is rarely if ever conducted in health studies – a moderating effect test. I left out the “egg-yolk years” variable used by the authors, and focused on weekly egg consumption (see Chris’s critique). My analysis, using WarpPLS (), had to be done only visually, because using values from Table 2 meant that I had access only to data on a few variables organized in quintiles. That is, my analysis here using aggregate data is an N=5 analysis; a small sample indeed. The full-text article is not available publicly; Zoe was kind enough to include the data from Table 2 in her critique post.

Below is the model that I used for the moderating effect test. It allowed me to look into the effect that the variable EggsWk (number of eggs consumed per week) had on the association between LDL (LDL cholesterol) and Plaque (carotid plaque). This type of effect, namely a moderating effect, is confusing to many people, because it is essentially the effect that a variable has on the effect of another variable on a third. Still, being confusing does not mean being less important. I should note that this type of effect is similar to a type of conditional association tested via Bayesian statistics – if one eats more eggs, what is the association between having a high LDL cholesterol and plaque buildup?

You can see what is happening visually on the graph below. The plot on the left side is for low weekly egg consumption. In it, the association between LDL cholesterol and plaque is positive – eating fewer eggs, plaque and LDL increase together. The plot on the right side is for high weekly egg consumption. In this second plot, the association between LDL cholesterol and plaque is negative – eating more eggs, plaque decreases as LDL increases. And what is the turning point? It is about 2.3 eggs per week.

So the “evil” particle, the LDL, is playing tricks with us; but thankfully the wonderful eggs come to the rescue, right? Well, it looks a bit like it, but maybe other foods would have a similar effect. In part because of the moderating effect discussed above, the multivariate association between LDL cholesterol and plaque was overall negative. This multivariate association was estimated controlling for the moderating effect of weekly egg consumption. You can see this on the plot below.

The highest amount of plaque is at the far left of the plot. It is associated with the lowest LDL cholesterol quintile. (So much for eggs causing plaque via LDL cholesterol eh!?) What is happening here? Maybe egg consumption above a certain level shifts the size of the LDL particles from small to large, making the potentially atherogenic ones harmless. (Saturated fat consumption, in the context of a nutritious diet in lean individuals, seems to have a similar effect.) Maybe eggs contain nutrients that promote overall health, leading LDL particles to "behave" and do what they are supposed to do. Maybe it is a combination of these and other effects.

Large LDL and small HDL particles: The best combination

High-density lipoprotein (HDL) is one of the five main types of lipoproteins found in circulation, together with very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), low-density lipoprotein (LDL), and chylomicrons.

After a fatty meal, the blood is filled with chylomicrons, which carry triglycerides (TGAs). The TGAs are transferred to cells from chylomicrons via the activity of enzymes, in the form of free fatty acids (FFAs), which are used by those cells as sources of energy.

After delivering FFAs to the cells, the chylomicrons progressively lose their TGA content and “shrink”, eventually being absorbed and recycled by the liver. The liver exports part of the TGAs that it gets from chylomicrons back to cells for use as energy as well, now in the form of VLDL. As VLDL particles deliver TGAs to the cells they shrink in size, similarly to chylomicrons. As they shrink, VLDL particles first become IDL and then LDL particles.

The figure below (click on it to enlarge), from Elliott & Elliott (2009; reference at the end of this post), shows, on the same scale: (a) VLDL particles, (b) chylomicrons, (c) LDL particles, and (d) HDL particles. The dark bar at the bottom of each shot is 1000 A in length, or 100 nm (A = angstrom; nm = nanometer; 1 nm = 10 A).

As you can see from the figure, most of the LDL particles shown are about 1/4 of the length of the dark bar in diameter, often slightly more, or about 25-27 nm in size. They come in different sizes, with sizes in this range  being the most common. The smaller and denser they are, the more likely they are to contribute to the formation of atherosclerotic plaque in the presence of other factors, such as chronic inflammation. The larger they become, which usually happens in diets high in saturated fat, the less likely they are to form plaque.

Note that the HDL particles are rather small compared to the LDL particles. Shouldn’t they cause plaque then? Not really. Apparently they have to be small, compared to LDL particles, to do their job effectively.

HDL is a completely different animal from VLDL, IDL and LDL. HDL particles are produced by the liver as dense disk-like particles, known as nascent HDL particles. These nascent HDL particles progressively pick up cholesterol from cells, as well as performing a number of other functions, and “fatten up” with cholesterol in the process.

This process also involves HDL particles picking up cholesterol from plaque in the artery walls, which is one of the reasons why HDL cholesterol is informally called “good” cholesterol. In fact, neither HDL nor LDL are really cholesterol; HDL and LDL are particles that carry cholesterol, protein and fat.

As far as particle size is concerned, LDL and HDL are opposites. Large LDL particles are the least likely to cause plaque formation, because LDL particles have to be approximately 25 nm in diameter or smaller to penetrate the artery walls. With HDL the opposite seems to be true, as HDL particles need to be small (compared with LDL particles) to easily penetrate the artery walls in order to pick up cholesterol, leave the artery walls with their cargo, and have it returned back to the liver.

Another interesting aspect of this cycle is that the return to the liver of cholesterol picked up by HDL appears to be done largely via IDL and LDL particles (Elliott & Elliott, 2009), which get the cholesterol directly from HDL particles! Life is not that simple.

Reference:

William H. Elliott & Daphne C. Elliott (2009). Biochemistry and Molecular Biology. 4th Edition. New York: NY: Oxford University Press.

The China Study II: Cholesterol seems to protect against cardiovascular disease

First of all, many thanks are due to Dr. Campbell and his collaborators for collecting and compiling the data used in this analysis. The data has been compliled by those researchers to disseminate the data from a study often referred to as the “China Study II”. It has already been analyzed by other bloggers. Notable analyses have been conducted by Ricardo at Canibais e Reis, Stan at Heretic, and Denise at Raw Food SOS.

The analyses in this post differ from those other analyses in various aspects. One of them is that data for males and females were used separately for each county, instead of the totals per county. Only two data points per county were used (for males and females). This increased the sample size of the dataset without artificially reducing variance (for more details, see “Notes” at the end of the post), which is desirable since the dataset is relatively small. This also allowed for the test of commonsense assumptions (e.g., the protective effects of being female), which is always a good idea in a complex analysis because violation of commonsense assumption may suggest data collection or analysis error. On the other hand, it required the inclusion of a sex variable as a control variable in the analysis, which is no big deal.

The analysis was conducted using WarpPLS. Below is the model with the main results of the analysis. (Click on it to enlarge. Use the "CRTL" and "+" keys to zoom in, and CRTL" and "-" to zoom out.) The arrows explore associations between variables, which are shown within ovals. The meaning of each variable is the following: SexM1F2 = sex, with 1 assigned to males and 2 to females; HDLCHOL = HDL cholesterol; TOTCHOL = total cholesterol; MSCHIST = mortality from schistosomiasis infection; and MVASC = mortality from all cardiovascular diseases.

The variables to the left of MVASC are the main predictors of interest in the model – HDLCHOL and TOTCHOL. The ones to the right are control variables – SexM1F2 and MSCHIST. The path coefficients (indicated as beta coefficients) reflect the strength of the relationships. A negative beta means that the relationship is negative; i.e., an increase in a variable is associated with a decrease in the variable that it points to. The P values indicate the statistical significance of the relationship; a P lower than 0.05 generally means a significant relationship (95 percent or higher likelihood that the relationship is “real”).

In summary, this is what the model above is telling us:

- As HDL cholesterol increases, total cholesterol increases significantly (beta=0.48; P<0.01). This is to be expected, as HDL is a main component of total cholesterol, together with VLDL and LDL cholesterol.

- As total cholesterol increases, mortality from all cardiovascular diseases decreases significantly (beta=-0.25; P<0.01). This is to be expected if we assume that total cholesterol is in part an intervening variable between HDL cholesterol and mortality from all cardiovascular diseases. This assumption can be tested through a separate model (more below). Also, there is more to this story, as noted below.

- The effect of HDL cholesterol on mortality from all cardiovascular diseases is nonsignificant when we control for the effect of total cholesterol (beta=-0.08; P=0.26). This suggests that HDL’s protective role is subsumed by the variable total cholesterol, and also that it is possible that there is something else associated with total cholesterol that makes it protective. Otherwise the effect of total cholesterol might have been nonsignificant, and the effect of HDL cholesterol significant (the reverse of what we see here).

- Being female is significantly associated with a reduction in mortality from all cardiovascular diseases (beta=-0.16; P=0.01). This is to be expected. In other words, men are women with a few design flaws. (This situation reverses itself a bit after menopause.)

- Mortality from schistosomiasis infection is significantly and inversely associated with mortality from all cardiovascular diseases (beta=-0.28; P<0.01). This is probably due to those dying from schistosomiasis infection not being entered in the dataset as dying from cardiovascular diseases, and vice-versa.

Two other main components of total cholesterol, in addition to HDL cholesterol, are VLDL and LDL cholesterol. These are carried in particles, known as lipoproteins. VLDL cholesterol is usually represented as a fraction of triglycerides in cholesterol equations (e.g., the Friedewald and Iranian equations). It usually correlates inversely with HDL; that is, as HDL cholesterol increases, usually VLDL cholesterol decreases. Given this and the associations discussed above, it seems that LDL cholesterol is a good candidate for the possible “something else associated with total cholesterol that makes it protective”. But waidaminet! Is it possible that the demon particle, the LDL, serves any purpose other than giving us heart attacks?

The graph below shows the shape of the association between total cholesterol (TOTCHOL) and mortality from all cardiovascular diseases (MVASC). The values are provided in standardized format; e.g., 0 is the average, 1 is one standard deviation above the mean, and so on. The curve is the best-fitting S curve obtained by the software (an S curve is a slightly more complex curve than a U curve).

The graph below shows some of the data in unstandardized format, and organized differently. The data is grouped here in ranges of total cholesterol, which are shown on the horizontal axis. The lowest and highest ranges in the dataset are shown, to highlight the magnitude of the apparently protective effect. Here the two variables used to calculate mortality from all cardiovascular diseases (MVASC; see “Notes” at the end of this post) were added. Clearly the lowest mortality from all cardiovascular diseases is in the highest total cholesterol range, 172.5 to 180; and the highest mortality in the lowest total cholesterol range, 120 to 127.5. The difference is quite large; the mortality in the lowest range is approximately 3.3 times higher than in the highest.

The shape of the S-curve graph above suggests that there are other variables that are confounding the results a bit. Mortality from all cardiovascular diseases does seem to generally go down with increases in total cholesterol, but the smooth inflection point at the middle of the S-curve graph suggests a more complex variation pattern that may be influenced by other variables (e.g., smoking, dietary patterns, or even schistosomiasis infection; see “Notes” at the end of this post).

As mentioned before, total cholesterol is strongly influenced by HDL cholesterol, so below is the model with only HDL cholesterol (HDLCHOL) pointing at mortality from all cardiovascular diseases (MVASC), and the control variable sex (SexM1F2).

The graph above confirms the assumption that HDL’s protective role is subsumed by the variable total cholesterol. When the variable total cholesterol is removed from the model, as it was done above, the protective effect of HDL cholesterol becomes significant (beta=-0.27; P<0.01). The control variable sex (SexM1F2) was retained even in this targeted HDL effect model because of the expected confounding effect of sex; females generally tend to have higher HDL cholesterol and less cardiovascular disease than males.

Below, in the “Notes” section (after the “Reference”) are several notes, some of which are quite technical. Providing them separately hopefully has made the discussion above a bit easier to follow. The notes also point at some limitations of the analysis. This data needs to be analyzed from different angles, using multiple models, so that firmer conclusions can be reached. Still, the overall picture that seems to be emerging is at odds with previous beliefs based on the same dataset.

What could be increasing the apparently protective HDL and total cholesterol in this dataset? High consumption of animal foods, particularly foods rich in saturated fat and cholesterol, are strong candidates. Low consumption of vegetable oils rich in linoleic acid, and of foods rich in refined carbohydrates, are also good candidates. Maybe it is a combination of these.

We need more analyses!


Notes:

- The path coefficients (indicated as beta coefficients) reflect the strength of the relationships; they are a bit like standard univariate (or Pearson) correlation coefficients, except that they take into consideration multivariate relationships (they control for competing effects on each variable).

- The R-squared values reflect the percentage of explained variance for certain variables; the higher they are, the better the model fit with the data. In complex and multi-factorial phenomena such as health-related phenomena, many would consider an R-squared of 0.20 as acceptable. Still, such an R-squared would mean that 80 percent of the variance for a particularly variable is unexplained by the data.

- The P values have been calculated using a nonparametric technique, a form of resampling called jackknifing, which does not require the assumption that the data is normally distributed to be met. This and other related techniques also tend to yield more reliable results for small samples, and samples with outliers (as long as the outliers are “good” data, and are not the result of measurement error).

- Colinearity is an important consideration in models that analyze the effect of multiple predictors on one single variable. This is particularly true for multiple regression models, where there is a temptation of adding many predictors to the model to see which ones come out as the “winners”. This often backfires, as colinearity can severely distort the results. Some multiple regression techniques, such as automated stepwise regression with backward elimination, are particularly vulnerable to this problem. Colinearity is not the same as correlation, and thus is defined and measured differently. Two predictor variables may be significantly correlated and still have low colinearity. A reasonably reliable measure of colinearity is the variance inflation factor. Colinearity was tested in this model, and was found to be low.

- An effort was made here to avoid multiple data points per county (even though this was available for some variables), because this could artificially reduce the variance for each variable, and potentially bias the results. The reason for this is that multiple answers from a single county would normally be somewhat correlated; a higher degree of intra-county correlation than inter-county correlation. The resulting bias would be difficult to control for, via one or more control variables. With only two data points per county, one for males and the other for females, one can control for intra-country correlation by adding a “dummy” sex variable to the analysis, as a control variable. This was done here.

- Mortality from schistosomiasis infection (MSCHIST) is a variable that tends to affect the results in a way that makes it more difficult to make sense of them. Generally this is true for any infectious diseases that significantly affect a population under study. The problem with infection is that people with otherwise good health or habits may get the infection, and people with bad health and habits may not. Since cholesterol is used by the human body to fight disease, it may go up, giving the impression that it is going up for some other reason. Perhaps instead of controlling for its effect, as done here, it would have been better to remove from the analysis those counties with deaths from schistosomiasis infection. (See also this post, and this one.)

- Different parts of the data were collected at different times. It seems that the mortality data is for the period 1986-88, and the rest of the data is for 1989. This may have biased the results somewhat, even though the time lag is not that long, especially if there were changes in certain health trends from one period to the other. For example, major migrations from one county to another could have significantly affected the results.

- The following measures were used, from the China Study II dataset, like the other measures. P002 HDLCHOL, for HDLCHOL; P001 TOTCHOL, for TOTCHOL; and M021 SCHISTOc, for MSCHIST.

- SexM1F2 is a “dummy” variable that was coded with 1 assigned to males and 2 to females. As such, it essentially measures the “degree of femaleness” of the respondents. Being female is generally protective against cardiovascular disease, a situation that reverts itself a bit after menopause.

- MVASC is a composite measure of the two following variables, provided as component measures of mortality from all cardiovascular diseases: M058 ALLVASCb (ages 0-34), and M059 ALLVASCc (ages 35-69). A couple of obvious problems: (a) they does not include data on people older than 69; and (b) they seem to capture a lot of diseases, including some that do not seem like typical cardiovascular diseases. A factor analysis was conducted, and the loadings and cross-loadings suggested good validity. Composite reliability was also good. So essentially MVASC is measured here as a “latent variable” with two “indicators”. Why do this? The reason is that it reduces the biasing effects of incomplete data and measurement error (e.g., exclusion of folks older than 69). By the way, there is always some measurement error in any dataset.

- This note is related to measurement error in connection with the indicators for MVASC. There is something odd about the variables M058 ALLVASCb (ages 0-34), and M059 ALLVASCc (ages 35-69). According to the dataset, mortality from cardiovascular diseases for ages 0-34 is typically higher than for 35-69, for many counties. Given the good validity and reliability for MVASC as a latent variable, it is possible that the values for these two indicator variables were simply swapped by mistake.

The man who ate 25 eggs per day: What does this case really tell us?

Many readers of this blog have probably heard about the case of the man who ate approximately 25 eggs (20 to 30) per day for over 15 years (probably well over), was almost 90 years old (88) when the case was published in the prestigious The New England Journal of Medicine, and was in surprisingly good health ().

The case was authored by the late Dr. Fred Kern, Jr., a widely published lipid researcher after whom the Kern Lipid Conference is named (). One of Kern’s research interests was bile, a bitter-tasting fluid produced by the liver (and stored in the gallbladder) that helps with the digestion of lipids in the small intestine. He frames the man’s case in terms of a compensatory adaptation tied to bile secretion, arguing that this man was rather unique in his ability to deal with a lethal daily dose of dietary cholesterol.

Kern seemed to believe that dietary cholesterol was harmful, but that this man was somehow “immune” to it. This is ironic, because often this case is presented as evidence against the hypothesis that dietary cholesterol can be harmful. The table below shows the general nutrient content of the man’s daily diet of eggs. The numbers in this and other tables are based on data from Nutritiondata.com (), in some cases triangulated with other data. The 5.3 g of cholesterol in the table (i.e., 5,300 mg) is 1,775 percent the daily value recommended by the Institute of Medicine of the U.S. National Academy of Sciences ().

As you can see, the man was on a very low carbohydrate diet with a high daily intake of fat and protein. The man is described as an: “… 88-year-old man who lived in a retirement community [and] complained only of loneliness since his wife's death. He was an articulate, well-educated elderly man, healthy except for an extremely poor memory without other specific neurologic deficits … His general health had been excellent, without notable symptoms. He had mild constipation.”

The description does not suggest inherited high longevity: “His weight had been constant at 82 to 86 kg (height, 1.87 m). He had no history (according to the patient and his personal physician of 15 years) of heart disease, stroke, or kidney disease … The patient had never smoked and never drank excessively. His father died of unknown causes at the age of 40, and his mother died at 76 … He kept a careful record, egg by egg, of the number ingested each day …”

The table below shows the fat content of the man’s daily diet of eggs. With over 14 g of omega-6 fat intake every day, this man was probably close to or in “industrial seed oils territory” (), as far as daily omega-6 fat intake is concerned. And the intake of omega-3 fats, at less than 1 g, was not nearly enough to balance it. However, here is a relevant fact – this man was not consuming any industrial seed oils. He liked his eggs soft-boiled, which is why the numbers in this post refer to boiled eggs.

This man weighed between 82 to 86 kg, which is about 180 to 190 lbs. His height was 1.87 m, or about 6 ft 1 in. Therefore his body mass index varied between approximately 23 and 25, which is in the normal range. In other words, this person was not even close to obese during the many years he consumed 25 eggs or so per day. In the comments section of a previous post, on the sharp increase in obesity since the 1980s (), several readers argued that the sharp increase in obesity was very likely caused by an increase in omega-6 fat consumption.

I am open to the idea that industrialized omega-6 fats played a role in the sharp increase in obesity observed since the 1980s. When it comes to omega-6 fat consumption in general, including that in “more natural” foods (e.g., poultry and eggs), I am more skeptical. Still, it is quite possible that a diet high in omega-6 fats in general is unhealthy primarily if it is devoid of other nutrients. This man’s overall diet might have been protective not because of what he was not eating, but because of what he was eating.

The current debates pitting one diet against another often revolve around the ability of one diet or another to eliminate or reduce the intake of a “bad thing” (e.g., cholesterol, saturated fat, carbohydrates). Perhaps the discussion should be more focused on, or at least not completely ignore, what one diet or another include as protective factors. This would help better explain “odd findings”, such as the lowest-mortality body mass index of 26 in urban populations (). It would also help better explain “surprising cases”; such as this 25-eggs-a-day man’s, vegetarian-vegan “ageless woman” Annette Larkins’s (), and the decidedly carnivore De Vany couple’s ().

The table below shows the vitamin content of the man’s daily diet of eggs. The vitamin K2 content provided by Nutritiondata.com was incorrect; I had to get what seems to be the right number by triangulating values taken from various publications. And here we see something interesting. This man was consuming approximately the equivalent in vitamin K2 that one would get by eating 4 ounces of foie gras () every day. Foie gras, the fatty liver of overfed geese, is the richest known animal source of vitamin K2. This man’s diet was also high in vitamin A, which is believed to act synergistically with vitamin K2 – see Chris Masterjohn’s article on Weston Price’s “activator X” ().

Kern argued that the very high intake of dietary cholesterol led to a sharp increase in bile secretion, as the body tried to “get rid” of cholesterol (which is used in the synthesis of bile). However, the increased bile secretion might have been also been due to the high fat content of this man’s diet, since one of the main functions of bile is digestion of fats. Whatever the case may be, increased bile secretion leads to increased absorption of fat-soluble vitamins, and vitamins K2 and A are fat-soluble vitamins that seem to be protective against cardiovascular disease, cancer and other degenerative diseases.

Finally, the table below shows the mineral content of the man’s daily diet of eggs. As you can see, this man consumed 550 percent the officially recommended daily intake of selenium. This intake was slightly lower than the 400 micrograms per day purported to cause selenosis in adults (). Similarly to vitamins K2 and A, selenium seems to be protective against cardiovascular disease, cancer and other degenerative diseases. This man’s diet was also rich in phosphorus, needed for healthy teeth and bones.

Not too many people live to be 88 years of age; many fewer reach that age in fairly good health. The country with the highest average life expectancy in the world at the time of this writing is Japan, with a life expectancy of about 82 years (79 for men, and 86 for women). Those who think that they need a high HDL cholesterol and a low LDL cholesterol to be in good health, and thus live long lives, may be surprised at this man’s lipid profile: “The patient's plasma lipid levels were normal: total cholesterol, 5.18 mmol per liter (200 mg per deciliter); LDL, 3.68 mmol per liter (142 mg per deciliter); and HDL, 1.17 mmol per liter (45 mg per deciliter). The ratio of LDL to HDL cholesterol was 3.15.”

If we assume that this man is at least somewhat representative of the human species, and not a major exception as Kern argued, this case tells us that a diet of 25 eggs per day followed by over 15 years may actually be healthy for humans. Such diet has the following features:

- It is very high in dietary cholesterol.

- It involves a high intake of omega-6 fats from animal sources, with none coming from industrial seed oils.

- It involves a high overall intake of fats, including saturated fats.

- It is fairly high in protein, all of which from animal sources.

- It is a very low carbohydrate diet, with no sugar in it.

- It is a nutritious diet, rich in vitamins K2 and A, as well as in selenium and phosphorus.

This man ate 25 eggs per day apparently due to an obsession tied to mental problems. Repeated attempts at changing his behavior were unsuccessful. He said: “Eating these eggs ruins my life, but I can't help it.”

Total cholesterol and cardiovascular disease: A U-curve relationship

The hypothesis that blood cholesterol levels are positively correlated with heart disease (the lipid hypothesis) dates back to Rudolph Virchow in the mid-1800s.

One famous study that supported this hypothesis was Ancel Keys's Seven Countries Study, conducted between the 1950s and 1970s. This study eventually served as the foundation on which much of the advice that we receive today from doctors is based, even though several other studies have been published since that provide little support for the lipid hypothesis.

The graph below (from O Primitivo) shows the results of one study, involving many more countries than Key's Seven Countries Study, that actually suggests a NEGATIVE linear correlation between total cholesterol and cardiovascular disease.

Now, most relationships in nature are nonlinear, with quite a few following a pattern that looks like a U-curve (plain or inverted); sometimes called a J-curve pattern. The graph below (also from O Primitivo) shows the U-curve relationship between total cholesterol and mortality, with cardiovascular disease mortality indicated through a dotted red line at the bottom.

This graph has been obtained through a nonlinear analysis, and I think it provides a better picture of the relationship between total cholesterol (TC) and mortality. Based on this graph, the best range of TC that one can be at is somewhere between 210, where cardiovascular disease mortality is minimized; and 220, where total mortality is minimized.

The total mortality curve is the one indicated through the full blue line at the top. In fact, it suggests that mortality increases sharply as TC decreases below 200.

Now, these graphs relate TC with disease and mortality, and say nothing about LDL cholesterol (LDL). In my own experience, and that of many people I know, a TC of about 200 will typically be associated with a slightly elevated LDL (e.g., 110 to 150), even if one has a high HDL cholesterol (i.e., greater than 60).

Yet, most people who have a LDL greater than 100 will be told by their doctors, usually with the best of the intentions, to take statins, so that they can "keep their LDL under control". (LDL levels are usually calculated, not measured directly, which itself creates a whole new set of problems.)

Alas, reducing LDL to 100 or less will typically reduce TC below 200. If we go by the graphs above, especially the one showing the U-curves, these folks' risk for cardiovascular disease and mortality will go up - exactly the opposite effect that they and their doctors expected. And that will cost them financially as well, as statin drugs are expensive, in part to pay for all those TV ads.

Vitamin D production from UV radiation: The effects of total cholesterol and skin pigmentation

Our body naturally produces as much as 10,000 IU of vitamin D based on a few minutes of sun exposure when the sun is high. Getting that much vitamin D from dietary sources is very difficult, even after “fortification”.

The above refers to pre-sunburn exposure. Sunburn is not associated with increased vitamin D production; it is associated with skin damage and cancer.

Solar ultraviolet (UV) radiation is generally divided into two main types: UVB (wavelength: 280–320 nm) and UVA (320–400 nm). Vitamin D is produced primarily based on UVB radiation. Nevertheless, UVA is much more abundant, amounting to about 90 percent of the sun’s UV radiation.

UVA seems to cause the most skin damage, although there is some debate on this. If this is correct, one would expect skin pigmentation to be our body’s defense primarily against UVA radiation, not UVB radiation. If so, one’s ability to produce vitamin D based on UVB should not go down significantly as one’s skin becomes darker.

Also, vitamin D and cholesterol seem to be closely linked. Some argue that one is produced based on the other; others that they have the same precursor substance(s). Whatever the case may be, if vitamin D and cholesterol are indeed closely linked, one would expect low cholesterol levels to be associated with low vitamin D production based on sunlight.

Bogh et al. (2010) published a very interesting study; one of those studies that remain relevant as time goes by. The link to the study was provided by Ted Hutchinson in the comments sections of a another post on vitamin D. The study was published in a refereed journal with a solid reputation, the Journal of Investigative Dermatology.

The study by Bogh et al. (2010) is particularly interesting because it investigates a few issues on which there is a lot of speculation. Among the issues investigated are the effects of total cholesterol and skin pigmentation on the production of vitamin D from UVB radiation.

The figure below depicts the relationship between total cholesterol and vitamin D production based on UVB radiation. Vitamin D production is referred to as “delta 25(OH)D”. The univariate correlation is a fairly high and significant 0.51.

25(OH)D is the abbreviation for calcidiol, a prehormone that is produced in the liver based on vitamin D3 (cholecalciferol), and then converted in the kidneys into calcitriol, which is usually abbreviated as 1,25-(OH)2D3. The latter is the active form of vitamin D.

The table below shows 9 columns; the most relevant ones are the last pair at the right. They are the delta 25(OH)D levels for individuals with dark and fair skin after exposure to the same amount of UVB radiation. The difference in vitamin D production between the two groups is statistically indistinguishable from zero.

So there you have it. According to this study, low total cholesterol seems to be associated with impaired ability to produce vitamin D from UVB radiation. And skin pigmentation appears to have little effect on the amount of vitamin D produced.

The study has a few weaknesses, as do almost all studies. For example, if you take a look at the second pair of columns from the right on the table above, you’ll notice that the baseline 25(OH)D is lower for individuals with dark skin. The difference was just short of being significant at the 0.05 level.

What is the problem with that? Well, one of the findings of the study was that lower baseline 25(OH)D levels were significantly associated with higher delta 25(OH)D levels. Still, the baseline difference does not seem to be large enough to fully explain the lack of difference in delta 25(OH)D levels for individuals with dark and fair skin.

A widely cited dermatology researcher, Antony Young, published an invited commentary on this study in the same journal issue (Young, 2010). The commentary points out some weaknesses in the study, but is generally favorable. The weaknesses include the use of small sub-samples.

References

Bogh, M.K.B., Schmedes, A.V., Philipsen, P.A., Thieden, E., & Wulf, H.C. (2010). Vitamin D production after UVB exposure depends on baseline vitamin D and total cholesterol but not on skin pigmentation. Journal of Investigative Dermatology, 130(2), 546–553.

Young, A.R. (2010). Some light on the photobiology of vitamin D. Journal of Investigative Dermatology, 130(2), 346–348.

Want to improve your cholesterol profile? Replace refined carbs and sugars with saturated fat and cholesterol in your diet

An interesting study by Clifton and colleagues (1998; full reference and link at the end of this post) looked at whether LDL cholesterol particle size distribution at baseline (i.e., beginning of the study) for various people was a determinant of lipid profile changes in each of two diets – one low and the other high in fat. This study highlights a few interesting points made in a previous post, which are largely unrelated to the main goal or findings of the study, but that are supported by side findings:

- As one increases dietary cholesterol and fat consumption, particularly saturated fat, circulating HDL cholesterol increases significantly. This happens whether one is taking niacin or not, although niacin seems to help, possibly as an independent (not moderating) factor. Increasing serum vitamin D levels, which can be done through sunlight exposure and supplementation, are also known to increase circulating HDL cholesterol.

- As one increases dietary cholesterol and fat consumption, particularly saturated fat, triglycerides in the fasting state (i.e., measured after a 8-hour fast) decrease significantly, particularly on a low carbohydrate diet. Triglycerides in the fasting state are negatively correlated with HDL cholesterol; they go down as HDL cholesterol goes up. This happens whether one is taking niacin or supplementing omega 3 fats or not, although these seem to help, possibly as independent factors.

- If one increases dietary fat intake, without also decreasing carbohydrate intake (particularly in the form of refined grains and sugars), LDL cholesterol will increase. Even so, LDL particle sizes will shift to more benign forms, which are the larger forms. Not all LDL particles change to benign forms, and there seem to be some genetic factors that influence this. LDL particles larger than 26 nm in diameter simply cannot pass through the gaps in the endothelium, which is a thin layer of cells lining the interior surface of arteries, and thus do not induce plaque formation.

The study by Clifton and colleagues (1998) involved 54 men and 51 women with a wide range of lipid profiles. They first underwent a 2-week low fat period, after which they were given two liquid supplements in addition to their low fat diet, for a period of 3 weeks. One of the liquid supplements contained 31 to 40 g of fat, and 650 to 845 mg of cholesterol. The other was fat and cholesterol free.

Studies that adopt a particular diet at baseline have the advantage of departing from a uniform diet across conditions. They also typically have one common characteristic: the baseline diet reflects the beliefs of the authors about what an ideal diet is. That is not always the case, of course. If this was indeed the case here, we have a particularly interesting study, because in that case the side findings discussed below contradicted the authors’ beliefs.

The table below shows the following measures for the participants in the study: age, body mass index (BMI), waist-to-hip ratio (WHR), total cholesterol, triglycerides, low-density lipoprotein (LDL) cholesterol, and three subtypes of high-density lipoprotein (HDL) cholesterol. LDL cholesterol is the colloquially known as the “bad” type, and “HDL” as the good one (which is an oversimplification). In short, the participants were overweight, middle-aged men and women, with relatively poor lipid profiles.

At the bottom of the table is the note “P < 0.001”, following a small “a”. This essentially means that on the rows indicated by an “a”, like the “WHR” row, the difference in the averages (e.g., 0.81 for women, and 0.93 for men, in the WHR row) was significantly different from what one would expect it to be due to chance alone. More precisely, the likelihood that the difference was due to chance was lower than 0.001, or 0.1 percent, in the case of a P < 0.001. Usually a difference between averages (a.k.a. means) associated with a P < 0.05 will be considered statistically significant.

Since the LDL cholesterol concentrations (as well as other lipoprotein concentrations) are listed on the table in mmol/L, and many people receive those measures in mg/dL in blood lipid profile test reports, below is a conversion table for LDL cholesterol (from: Wikipedia).

The table below shows the dietary intake in the low and high fat diets. Note that in the high fat diet, not only is the fat intake higher, but so is the cholesterol intake. The latter is significantly higher, more than 4 times the intake in the low fat diet, and about 2.5 times the recommended daily value by the U.S. Food and Drug Administration. The total calorie intake is reported as slightly lower in the high fat diet than in the low fat diet.

Note that the largest increase was in saturated fat, followed by an almost equally large increase in monounsaturated fat. This, together with the increase in cholesterol, mimics a move to a diet where fatty meat and organs are consumed in higher quantities, with a corresponding reduction in the intake of refined carbohydrates (e.g., bread, pasta, sugar, potatoes) and lean meats.

Finally, the table below shows the changes in lipid profiles in the low and high fat diets. Note that all subtypes of HDL (or "good") cholesterol concentrations were significantly higher in the high fat diet, which is very telling, because HDL cholesterol concentrations are much better predictors of cardiovascular disease than LDL or total cholesterol concentrations. The higher the HDL cholesterol, the lower the risk of cardiovascular disease.

In the table above, we also see that triglycerides are significantly lower in the high fat diet, which is also good, because high fasting triglyceride concentrations are associated with cardiovascular disease and also insulin resistance (which is associated with diabetes).

However, the total and LDL cholesterol were also significantly higher in the high fat compared to the low fat diet. Is this as bad as it sounds? Not when we look at other factors that are not clear from the tables in the article.

One of those factors is the likely change in LDL particle size. LDL particle sizes almost always increase with significant increases in HDL; frequently going up in diameter beyond 26 nm, and thus passing the threshold beyond which an LDL particle can penetrate the endothelium and help form a plaque.

Another important factor to take into consideration is the somewhat strange decision by the authors to use the Friedewald equation to estimate the LDL concentrations in the low and high fat diets. Through the Friedewald equation, LDL is calculated as follows (where TC is total cholesterol):

    LDL = TC – HDL – Triglycerides / 5

Here is one of the problems with the Friedewald equation. Let us assume that an individual has the following lipid profile numbers: TC = 200, HDL = 50, and trigs. = 150. The calculated LDL will be 120. Let us assume that this same individual reduces trigs. to 50, from the previous 150, keeping all of the other measures constant. This is a major improvement. Yet, the calculated LDL will now be 140, and a doctor will tell this person to consider taking statins!

By the way, most people who do a blood test and get their lipid profile report also get their LDL calculated through the Friedewald equation. Usually this is indicated through a "CALC" note next to the description of the test or the calculated LDL number.

Finally, total cholesterol is not a very useful measure, because an elevated total cholesterol may be primarily reflecting an elevated HDL, which is healthy. Also, a slightly elevated total cholesterol seems to be protective, as it is associated with reduced overall mortality and also reduced mortality from cardiovascular disease, according to U-curve regression studies comparing mortality and total cholesterol levels in different countries.

We do not know for sure that the participants in this study were consuming a lot of refined carbohydrates and/or sugars at baseline. But it is a safe bet that they were, since they were consuming 214 g of carbohydrates per day. It is difficult, although not impossible, to eat that many carbohydrates per day by eating only vegetables and fruits, which are mostly water. Consumption of starches makes it easier to reach that level.

This is why when one goes on a paleo diet, he or she reduces significantly the amount of dietary carbohydrates; even more so on a targeted low carbohydrate diet, such as the Atkins diet. Richard K. Bernstein, who is a type 1 diabetic and has been adopting a strict low carbohydrate diet during most of his adult life, had the following lipid profile at 72 years of age: HDL = 118, LDL = 53, trigs. = 45. His fasting blood sugar was reportedly 83 mg/dl. Click here to listen to an interview with Dr. Bernstein on the The Livin' La Vida Low-Carb Show.

The lipid profile improvement observed (e.g., a 14 percent increase in HDL from baseline for men, and about half that for women, in only 3 weeks) was very likely due to an increase in dietary saturated fat and cholesterol combined with a decrease in refined carbohydrates and sugars. The improvement would probably have been even more impressive with a higher increase in saturated fat, as long as it was accompanied by the elimination of refined carbohydrates and sugars from the participants’ diets.

Reference:

Clifton, P. M., M. Noakes, and P. J. Nestel (1998). LDL particle size and LDL and HDL cholesterol changes with dietary fat and cholesterol in healthy subjects. J. Lipid. Res. 39: 1799–1804.

What should be my HDL cholesterol?

HDL cholesterol levels are a rough measure of HDL particle quantity in the blood. They actually tell us next to nothing about HDL particle type, although HDL cholesterol increases are usually associated with increases in LDL particle size. This a good thing, since small-dense LDL particles are associated with increased cardiovascular disease.

Most blood lipid panels reviewed by family doctors with patients give information about HDL status through measures of HDL cholesterol, provided in one of the standard units (e.g., mg/dl).

Study after study shows that HDL cholesterol levels, although imprecise, are a much better predictor of cardiovascular disease than LDL or total cholesterol levels. How high should be one’s HDL cholesterol? The answer to this question is somewhat dependent on each individual’s health profile, but most data suggest that a level greater than 60 mg/dl (1.55 mmol/l) is close to optimal for most people.

The figure below (from Eckardstein, 2008; full reference at the end of this post) plots incidence of coronary events in men (on the vertical axis), over a period of 10 years, against HDL cholesterol levels (on the horizontal axis). Note: IFG = impaired fasting glucose. This relationship is similar for women, particularly post-menopausal women. Pre-menopausal women usually have higher HDL cholesterol levels than men, and a low incidence of coronary events.

From the figure above, one can say that a diabetic man with about 55 mg/dl of HDL cholesterol will have approximately the same chance, on average, of having a coronary event (a heart attack) as a man with no risk factors and about 20 mg/dl of HDL cholesterol. That chance will be about 7 percent. With 20 mg/dl of HDL cholesterol, the chance of a diabetic man having a coronary event would approach 50 percent.

We can also conclude from the figure above that a man with no risk factors will have a 5 percent chance of having a coronary event if his HDL cholesterol is about 25 mg/dl; and about 2 percent if his HDL cholesterol is greater than 60 mg/dl. This a 60 percent reduction in risk, a risk that was low to start with because of the absence of risk factors.

HDL cholesterol levels greater than 60 are associated with significantly reduced risks of coronary events, particularly for those with diabetes (the graph does not take diabetes type into consideration). Much higher levels of HDL cholesterol (beyond 60) do not seem to be associated with much lower risk of coronary events.

Conversely, a very low HDL cholesterol level (below 25) is a major risk factor when other risk factors are also present, particularly: diabetes, hypertension (high blood pressure), and familial hypercholesteromia (gene-induced very elevated LDL cholesterol).

It is not yet clear whether HDL cholesterol is a cause of reduced cardiovascular disease, or just a marker of other health factors that lead to reduced risk for cardiovascular disease. Much of the empirical evidence suggests a causal relationship, and if this is the case then it may be a good idea to try to increase HDL levels. Even if HDL cholesterol is just a marker, the same strategy that increases it may also have a positive impact on the real causative factor of which HDL cholesterol is a marker.

What can one do to increase his or her HDL cholesterol? One way is to replace refined carbs and sugars with saturated fat and cholesterol in one’s diet. (I know that this sounds counterintuitive, but seems to work.) Another is to increase one’s vitamin D status, through sun exposure or supplementation.

Other therapeutic interventions can also be used to increase HDL; some more natural than others. The figure below (also from Eckardstein, 2008) shows the maximum effects of several therapeutic interventions to increase HDL cholesterol.

Among the therapeutic interventions shown in the figure above, taking nicotinic acid (niacin) in pharmacological doses, of 1 to 3 g per day (higher dosages may be toxic), is by far the most effective way of increasing one’s HDL cholesterol. Only the niacin that causes flush is effective in this respect. No-flush niacin preparations may have some anti-inflammatory effects, but do not cause increases in HDL cholesterol.

Rimonabant, which is second to niacin in its effect on HDL cholesterol, is an appetite suppressor that has been associated with serious side effects and, to be best of my knowledge, has been largely banned from use in pharmaceutical drugs.

Third in terms of effectiveness, among the factors shown in the figure, is moderate alcohol consumption. Running about 19 miles per week (2.7 miles per day) and taking fibrates are tied in forth place.

Many people think that they are having a major allergic reaction, and have a panic attack, when they experience the niacin flush. This usually happens several minutes after taking niacin, and depends on the dose and whether niacin was consumed with food or not. It is not uncommon for one’s entire torso to turn hot red, as though the person had had major sunburn. This reaction is harmless, and usually disappears after several minutes.

One could say that, with niacin: no “pain” (i.e., flush), no gain.

Reference:

von Eckardstein, A. (2008). HDL – a difficult friend. Drug Discovery Today: Disease Mechanisms, 5(3), 315-324.

Niacin and its effects on growth hormone, glucagon, cortisol, blood lipids, mental disorders, and fasting glucose levels

Niacin is a very interesting vitamin. It is also known as vitamin B3, or nicotinic acid. It is an essential vitamin whose deficiency leads to a dreadful disease known as pellagra. In large doses of 1 to 3 g per day it has several effects on blood lipids, including these: it increases HDL cholesterol, decreases triglycerides, and decreases Lp(a). Given that this is essentially a reversal of the metabolic syndrome, for those who are on their way to developing it, niacin must really do something good for our body. Niacin is also a powerful antioxidant.

The lipid modification effects of niacin are so consistent across a broad spectrum of the population that some companies that commercialize niacin-based products guarantee some measure of those effects. The graphs below (click to enlarge) are from Arizona Pharmaceuticals, a company that commercializes an instant-release niacin formulation called Nialor (see: arizonapharmaceuticals.com). The graphs show the peak effects on HDL cholesterol and triglycerides at the recommended dose, which is 1.5 g per day. The company guarantees effects; not the peak effects shown, but effects that are large enough to have clinical significance.

Niacin also has been used in the treatment of various mental disorders, including schizophrenia. Its effectiveness in this domain (mental disease) is still under debate. Yet many people, including reputable mental health researchers, swear by it. Empirical research suggests beyond much doubt that niacin helps in the treatment of depression and bipolar disorder.

Abram Hoffer, a Canadian psychiatrist who died in 2009, at the age of 91, has discussed at length the many beneficial health effects of niacin. He was also a niacin user. He argued that it can even make people live longer, and be generally healthier and more active. The effect on longevity may sound far-fetched, but there is empirical data supporting this hypothesis as well. (For more, see this book.)

By the way, moderate niacin supplementation seems to increase the milk output of cows, without any effect on milk composition.

Most people dislike the sensation that is caused by niacin, the “niacin flush”. This is a temporary sensation similar to that of sunburn covering one’s full torso and face. It goes away after a few minutes. This is niacin’s main undesirable side effect at doses up to 3 g per day. Higher doses are not recommended, and can be toxic to the liver.

Nobody seems to understand very well how niacin works. This leads to some confusion. Many people think that niacin inhibits the production of VLDL, free fatty acids, and ketones; preventing the use of fat as an energy source. And it does!

So it makes you fat, right?

No, because these effects are temporary, and are followed, often after 3 to 5 hours, by a large increase in circulating growth hormone, cortisol and glucagon. These hormones are associated with (maybe they cause, maybe are caused by) a large increase in free fatty acids and ketones in circulation, but not with an increase in VLDL secretion by the liver. So ketosis is at first inhibited by niacin, and then comes in full force after a few hours.

The decreased VLDL secretion is no surprise, because VLDL is not really needed in large quantities if muscle tissues (including the heart) are being fed what they really like: free fatty acids and ketones. When VLDL particles are secreted by the liver in small numbers, they tend to be large. As they shrink in size after delivering their lipid content to muscle tissues, they become large LDL particles; too large to cross the endothelial gaps and cause plaque formation.

It is as if niacin held you back for a few hours, in terms of fat burning, and then released you with a strong push.

Since niacin does not seem to suppress the secretion of chylomicrons by the intestines, it should be taken with meals. The meals do not necessarily have to have any carbohydrates in them. If you take niacin while fasting, you may feel “funny” and somewhat weak, because of the decrease in VLDL, free fatty acids, and ketones in circulation. These, particularly the free fatty acids and ketones, are important sources of energy in the fasted state.

Given niacin’s delayed effects, it does not seem to make much sense to take slow release niacin of any kind. In fact, the form of niacin that seems to work best is the instant-release one, the one that gives you the flush. It may be a good idea to wait until 3 to 5 hours after you take it to do heavy exercise. You may feel a surge of energy 3 to 5 hours after taking it, when the delayed effects kick in.

The delayed effects of niacin on growth hormone, cortisol and glucagon are probably the reasons why people taking niacin frequently see a small increase in fasting glucose levels. This increase is usually of a few percentage points, but can be a bit higher in some people. Growth hormone, cortisol and particularly glucagon increase blood glucose levels; and the blood levels of these hormones naturally rise in the morning to get you ready for the day ahead. Niacin seems to boost that. Hence the increase in fasting blood glucose levels. This appears to be a benign effect, easily counterbalanced by niacin’s many benefits.

In spite of a possible increase in fasting glucose levels, there is no evidence that niacin increases average blood glucose levels. If it did, that would not be a good thing. In fact, it has been argued that niacin intake can be part of an effective approach to treating diabetes; Robert C. Atkins discussed this in his Vita-Nutrient Solution book.

Niacin’s effects on lipids are somewhat similar to those of low carbohydrate dieting. For example, both lead to a decrease in fasting triglycerides and an increase in HDL cholesterol. But the mechanisms by which those effects are achieved appear to be rather different.

References:

Quabbe, H.J., Trompke, M., & Luyckx, A.S. (1983). Influence of ketone body infusion on plasma growth hormone and glucagon in man. J. Clin Endocrinol Metab., 57(3):613-8.

Quabbe, H.J., Luyckx, A.S., L'age M., & Schwarz, C. (1983). Growth hormone, cortisol, and glucagon concentrations during plasma free fatty acid depression: different effects of nicotinic acid and an adenosine derivative (BM 11.189). J. Clin Endocrinol Metab., 57(2):410-4.

Schade, D.S., Woodside, W., & Eaton, R.P. (1979). The role of glucagon in the regulation of plasma lipids. Metabolism, 28(8):874-86.

Sudden cholesterol increase? It may be psychological

There are many published studies with evidence that cholesterol levels are positively associated with heart disease. In multivariate analyses the effects are usually small, but they are still there. On the other hand, there is also plenty of evidence that cholesterol is beneficial in terms of health. Here of course I am referring to the health of humans, not of the many parasites that benefit from disease.

For example, there is evidence () that cholesterol levels are negatively associated with mortality (i.e., higher cholesterol leading to lower mortality), and are positively associated with vitamin D production from skin exposure to sunlight ().

Most of the debris accumulated in atheromas are made up of macrophages, which are specialized cells that “eat” cell debris (ironically) and some pathogens. The drug market is still hot for cholesterol-lowering drugs, often presented in TV and Internet ads as effective tools to prevent formation of atheromas.

But what about macrophages? What about calcium, another big component of atheromas? If drugs were to target macrophages for atheroma prevention, drug users may experience major muscle wasting and problems with adaptive immunity, as macrophages play a key role in muscle repair and antibody formation. If drugs were to target calcium, users may experience osteoporosis.

So cholesterol is the target, because there is a “link” between cholesterol and atheroma formation. There is also a link between the number of house fires in a city and the amount of firefighting activity in the city, but we don’t see mayors announcing initiatives to reduce the number of firefighters in their cities to prevent house fires.

When we talk about variations in cholesterol, we usually mean variations in cholesterol carried by LDL particles. That is because LDL cholesterol seems to be very “sensitive” to a number of factors, including diet and disease, presenting quite a lot of sudden variation in response to changes in those factors.

LDL particles seem to be intimately involved with disease, but do not be so quick to conclude that they cause disease. Something so widespread and with so many functions in the human body could not be primarily an agent of disease that needs to be countered with statins. That makes no sense.

Looking at the totally of evidence linking cholesterol with health, it seems that cholesterol is extremely important for the human body, particularly when it is under attack. So the increases in LDL cholesterol associated with various diseases, notably heart disease, may not be because cholesterol is causing disease, but rather because cholesterol is being used to cope with disease.

LDL particles, and their content (including cholesterol), may be used by the body to cope with conditions that themselves cause heart disease, and end up being blamed in the process. The lipid hypothesis may be a classic case of reverse causation. A case in point is that of cholesterol responses to stress, particularly mental stress.

Grundy and Griffin () studied the effects of academic final examinations on serum cholesterol levels in 2 groups of medical students in the winter and spring semesters (see table below). During control periods, average cholesterol levels in the two groups were approximately 213 and 216 mg/dl. During the final examination periods, average cholesterol levels were 248 and 240 mg/dl. These measures were for winter and spring, respectively.



One could say that even the bigger increase from 213 to 248 is not that impressive in percentage terms, approximately 16 percent. However, HDL cholesterol does not go up significantly in response to sustained (e.g., multi-day) stress, it actually goes down, so the increases reported can be safely assumed to be chiefly due to LDL cholesterol. For most people, LDL particles are the main carriers of cholesterol in the human body. Thus, in percentage terms, the increases in LDL cholesterol are about twice those reported for total cholesterol.

A 32-percent increase (16 x 2) in LDL cholesterol would not go unnoticed today. If one’s LDL cholesterol were to be normally 140 mg/dl, it would jump to 185 mg/dl with a 32-percent increase. It looks like the standard deviations were more than 30 in the study. (This is based on the standard errors reported, and assuming that the standard deviation equals the standard error multiplied by the square root of the sample size.) So we can guess that several people might go from 140 to 215 or more (this is LDL cholesterol, in mg/dl) in response to the stress from exams.

And the effects above were observed with young medical students, in response to the stress from exams. What about a middle-aged man or woman trying to cope with chronic mental stress for months or years, due to losing his or her job, while still having to provide for a family? Or someone who has just been promoted, and finds himself or herself overwhelmed with the new responsibilities?

Keep in mind that sustained dieting can be a major stressor for some people, particular when one gets to that point in the dieting process where he or she gets regularly into negative nitrogen balance (muscle loss). So you may have heard from people saying that, after months or years of successful dieting, their cholesterol levels are inexplicably going up. Well, this post provides one of many possible explanations for that.

The finding that cholesterol goes up with stress has been replicated many times. It has been known for a long time, with studies dating back to the 1950s. Wertlake and colleagues () observed an increase in average cholesterol levels from 214 to 238 (in mg/dl); also among medical students, in response to the mental and emotional stress of an examination week. A similar study to the one above.

Those enamored with the idea of standing up the whole day, thinking that this will make them healthy, should know that performing cognitively demanding tasks while standing up is a known stressor. It is often used in research where stress must be induced to create an experimental condition. Muldoon and colleagues () found that people performing a mental task while standing experienced an increase in serum cholesterol of approximately 22 points (in mg/dl).

What we are not adapted for is sitting down for long hours in very comfortable furniture (, ). But our anatomy clearly suggests adaptations for sitting down, particularly when engaging in activities that resemble tool-making, a hallmark of the human species. Among modern hunter-gatherers, tool-making is part of daily life, and typically it is much easier to accomplish sitting down than standing up.

Modern urbanites could be seen as engaging in activities that resemble tool-making when they produce things at work for internal or external customers, whether those things are tangible or intangible.

So, stress is associated with cholesterol levels, and particularly with LDL cholesterol levels. Diehard lipid hypothesis proponents may argue that this is how stress is associated with heart disease: stress increases cholesterol which increases heart disease. Others may argue that one of the reasons why LDL cholesterol levels are sometimes found to be associated with heart disease-related conditions, such as chronic stress, and other health conditions is that the body is using LDL cholesterol to cope with those conditions.

Specifically regarding mental stress, a third argument has been put forth by Patterson and colleagues, who claimed that stress-mediated variations in blood lipid concentrations are a secondary result of decreased plasma volume. The cause, in their interpretation, was unspecified – “vascular fluid shifts”. However, when you look at the numbers reported in their study, you still see a marked increase in LDL cholesterol, even controlling for plasma volume. And this is all in response to “10 minutes of mental arithmetic with harassment” ().

I tend to think that the view that cholesterol increases with stress because cholesterol is used by the body to cope with stress is the closest to the truth. Among other things, stress increases the body’s overall protein demand, and cholesterol is used in the synthesis of many proteins. This includes proteins used for signaling, also known as hormones.

Cholesterol also seems to be a diet marker, tending to go up in high fat diets. This is easier to explain. High fat diets increase the demand for bile production, as bile is used in the digestion of fat. Most of the cholesterol produced by the human body is used to make bile.

The PCSK9 enzyme, LDL cholesterol, and cardiovascular diseases

Cardiovascular diseases are currently the leading cause of death in most developed countries. They are particularly common among seniors; i.e., those aged 65 and older. Part of the reason for this is that infectious diseases do not kill as many people as they used to.

Given the trend toward population aging, with seniors making up an increasingly larger percentage of the population, the market for drugs against cardiovascular diseases is growing. A new class of such drugs is making the news lately; they target the PCSK9 enzyme ().

Enzymes are (usually) proteins that speed up chemical reactions, and are needed in virtually all metabolic processes that occur in cells. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is an enzyme that degrades LDL cholesterol receptors on the surface of liver cells. Fewer LDL cholesterol receptors mean reduced uptake of the particles that carry LDL cholesterol, and thus more LDL particles in circulation. This may be problematic if these are small-dense LDL particles ().

Small-dense LDL particles include particles that are significantly smaller than the gaps in the endothelium (). The endothelium is a thin layer of cells that line the interior of arteries. Those gaps are about 25-26 nanometers (nm) in diameter. Small-dense LDL particles can contribute a lot more to the formation of atheromas (atherosclerotic plaques) in predisposed individuals than large-buoyant LDL particles.

There is evidence of the natural occurrence of low LDL cholesterol in individuals of African descent due to genetic mutations influencing PCSK9 levels (). This leads us to a very important question. By reducing PCSK9 in circulation, can we also reduce the incidence of cardiovascular disease?

The answer to this question depends on whether LDL cholesterol is a causative factor in cardiovascular disease. If it is, then reducing PCSK9 in circulation can indeed reduce the incidence of cardiovascular disease. The problem is that, most of the evidence so far suggests that LDL cholesterol is NOT a causative factor in cardiovascular disease.

Yes, there are studies that show that LDL cholesterol is correlated with cardiovascular disease, but the problem is that LDL cholesterol is a marker of other factors that are better candidates for causes of cardiovascular disease – hence the correlation. For example, LDL cholesterol goes up with mental stress (), and chronic mental stress seems to be a good candidate for a cause of cardiovascular disease.

LDL cholesterol is also a marker of a diet with more saturated fat in it (). In many contexts, a diet with more saturated fat in it is a more nutritious diet, which leads to a negative association between LDL cholesterol and mortality.

The graph below shows the shape of the association between total cholesterol (TOTCHOL) and mortality from all cardiovascular diseases (MVASC), based on an analysis of the China Study II dataset (). LDL cholesterol is the main component of total cholesterol in most people. The values are provided in standardized format; e.g., 0 is the average, 1 is one standard deviation above the mean, and so on. The best-fitting curve was obtained with the software WarpPLS ().

In fact, when we combine the totality of the evidence linking LDL cholesterol and cardiovascular diseases, LDL cholesterol seems to come out as a marker of protective factors. A reflection of this is a widely cited study by Weverling-Rijnsburger and colleagues, of LDL and HDL cholesterol as factors in cardiovascular diseases among people aged 85 and older (). The conclusions of the study were that:

- There was no association between LDL cholesterol level and risk of fatal cardiovascular disease.

- A low HDL cholesterol level was associated with a two-fold higher risk of fatal cardiovascular disease.

- Both low LDL cholesterol and low HDL cholesterol levels were associated with an increased mortality risk from infections.

The results above are particularly interesting because the study participants, given their ages, were at a high risk of mortality from cardiovascular diseases. It seems that the best scenario for these folks would have been a concomitant increase in both LDL and HDL cholesterol levels, which seems to be exactly what happens when one increases his or her intake of foods rich in saturated fat and dietary cholesterol ()!

Should you take a drug that targets the PCSK9 enzyme, to reduce your LDL cholesterol? Maybe you should ask Peter ().